Data labeling is adding correct answers, such as categories, tags, boxes or ratings, to raw examples. The labelled data is then used to train or evaluate a machine learning model.
Machine learning is a branch of AI in which systems learn patterns from data to make predictions or decisions. The behaviour comes from training on examples rather than from rules written by hand.
RLHF is a training method that uses people's preferences between model outputs to shape a model's behaviour. It is mainly used to make language models more helpful, honest and safe.
Training data is the set of examples a machine learning model learns from. Its quality, coverage and labelling largely determine how well the model performs.
Computer vision is the field of AI that lets software interpret images and video. Typical tasks are classifying objects, detecting defects, reading text and tracking movement.
Named entity recognition is the task of finding and labelling specific things in text, such as people, companies, places, dates, amounts or product codes. It turns free text into fields a system can store and search.
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